Cultural distance prediction system for media asset

ABSTRACT

Various embodiments described herein support or provide for cultural distance prediction operations of a media asset, such as determining events in a media asset, determining geographical region corresponding to the culture of origin and the culture of destination, accessing weight values of cultural attribute categories respectively associated with the geographical regions of the culture of origin and the culture of destination, generating cultural distance score of events, and causing display of the cultural distance score on a user interface of a client device.

RELATED APPLICATIONS

This application is a continuation of U.S. Pat. Application Serial No.17/336,229, filed Jun. 1, 2021, which application is incorporated hereinby reference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to media assets, and, moreparticularly, various embodiments described herein provide for systems,methods, techniques, instruction sequences, and devices that facilitatecultural distance prediction for a media asset, such as an audio assetor a video asset, based on a multi-dimensional hierarchy of culturalattributes associated with a geographical region where the media assetis generated, and a geographical region where the media asset istargeted for release.

BACKGROUND

The Media and Entertainment industry is experiencing unprecedentedglobal growth in content creation, distribution, and consumption. Filmand television content created in one country can now seek distributionin over two hundred countries and territories and can be enjoyed byworldwide audiences of various cultural backgrounds. The linguisticchallenge is not the only challenge for targeting overseas markets, thatadapting media content distributed to audiences of different culturalbackgrounds presents another barrier to success.

The worldwide number of film and television content released annually isgrowing exponentially. The rapid growth makes it difficult for humansalone to accurately and consistently adjust content based on localculture for distribution. Finding solutions for these challengesrequires deep domain expertise in Media and Entertainment, understandingof the cultural differences (e.g., cultural distances) and complexitiesof the global regulatory environment, and a vision for properlyengineered and trained machine learning (“ML”) and artificialintelligence (“AI”) systems. All of these solutions may help withincreasing the cultural appeal of the distributed content to audiencesin a distributed region different from a region where the content iscreated.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some embodiments are illustratedby way of example, and not limitation, in the figures of theaccompanying drawings.

FIG. 1 is a block diagram showing an example data system that includes acultural distance prediction system for media assets, according tovarious embodiments.

FIG. 2 is a block diagram illustrating an example cultural distanceprediction system for media assets, according to various embodiments.

FIG. 3 is a flowchart illustrating data flow within an example culturaldistance prediction system for media assets during operation, accordingto various embodiments.

FIGS. 4A-4B are flowcharts illustrating an example method for culturaldistance prediction of media assets, according to various embodiments.

FIG. 5 is a block diagram illustrating example cultural attributecategories in a multi-dimensional hierarchy of cultural attributes basedon a predetermined cultural attributes classification ontology ortaxonomy, according to various embodiments.

FIG. 6 illustrates an example set of weight values pre-determined forcultural attribute categories for each geographical region, according tovarious embodiments.

FIG. 7 illustrates an example customized cultural attribute graphgenerated based on an example event, for an example geographical region,according to various embodiments.

FIG. 8 illustrates an example graphical user interface showing acultural distance score generated for a media asset and scores generatedfor one or more events, scenes, themes, and genres of the media asset,according to various embodiments.

FIG. 9 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described, according to various embodiments.

FIG. 10 is a block diagram illustrating components of a machine able toread instructions from a machine storage medium and perform any one ormore of the methodologies discussed herein according to variousembodiments.

DETAILED DESCRIPTION

A successful global distribution strategy for media assets requires thecontent to be localized for each distributed region, so that it isappropriate and enjoyable to be consumed by local audiences of variouscultural backgrounds. A solution for cultural distance prediction is animportant tool for the content providers (e.g., content recommendationplatform providers) to determine if marketing content and the associatedmedia asset should be adjusted to increase the cultural appeal toaudiences in a distributed region different from a region where themedia asset was created.

Culture encompasses the social behavior and norms found in humansocieties, including beliefs, behaviors, languages, practices,expressions, and other characteristics shared by groups of people withina specific timeframe considered unique to members of a specificethnicity, race, or national origin. Cultural attributes may differlargely between two regions (or countries). The differences at theregion-or culture-level are used to determine cultural distance.

One of the major challenges presented for content providers is toprovide appropriate, personalized, and enjoyable content for audiences.Content providers attempt to recommend customized content based on userinterests, watching history, genre preferences to improve userexperience. Empirical studies indicate that one of the most importantfactors to consider when personalizing content is cultural identity,representing a user’s cultural origin and belonging.

Cultural identity serves as a more robust indicator when determining ifa given content is appealing to a user of specific cultural background.A cultural distance score generated based on attributes, such ascultural attributes of the regions where the content is created andwhere it is distributed, serves as a strong factor in the contentpersonalization decision process for each user So far, no existingcontent recommendation system provides a cultural identity-based contentpersonalization solution for users, assisted by the cultural distanceprediction system, as described herein.

Various embodiments described herein address these and otherdeficiencies of the conventional art. For example, various embodimentsdescribed herein can use state-of-art machine-learning (ML) andartificial intelligence (AI) to analyze and process millions of hours ofvideo content created daily in order to effectively identify events,scenes, themes, tropes, and genres of media assets, determine culturalattributes of regions associated with the media assets, and generatescultural distance scores for each identified events, scenes, themes,tropes, and genres.

In various embodiments, a cultural distance prediction system may accesscontent data of a given media asset. The media asset, such as an audioasset or a video asset, may be associated with a culture of origin,corresponding to a geographical region where the content is created. Themedia asset may also be associated with a culture of destination,corresponding to a geographical region where it is scheduled to bedistributed or released. The cultural distance prediction system maydetermine an event at a given timestamp based on the content data of themedia asset. Content data may include visual, audio, text, speechcontent, or some combination thereof, presented by the media asset.Based on the content data or context data of the media asset, the eventis determined to be relevant to a cultural attribute category. Thecultural attribute category corresponds to a multi-dimensional hierarchyof cultural attributes customized to each geographical region ofdistribution.

In various embodiments, the cultural distance prediction system mayidentify a first geographical region corresponding to the culture oforigin and a second geographical region corresponding to the culture ofdestination. For example, the first geographical region may be theUnited States, where the media asset was created. The secondgeographical region may be Japan, where the media asset is scheduled tobe distributed or released.

In various embodiments, the cultural distance prediction system mayaccess a first weight value of the cultural attribute categoryassociated with the first geographical region. In various embodiments,the cultural distance prediction system may access a second weight valueof the cultural attribute category associated with the secondgeographical region. In various embodiments, the first weight value andthe second weight value may be pre-determined absolute values,determined based on data available for the respective geographicalregions. In various embodiments, weight values of cultural attributesmay be pre-assigned or pre-determined based on data readily available tothe cultural distance prediction system. In various embodiment,pre-determined values are determined based on data curated ortransformed from regional artifacts, such as cultural attributes of thecorresponding category in a specific geographical region.

In various embodiments, the cultural distance prediction system may usea machine learning algorithm to generate a cultural distance score ofthe identified event, based on the first weight value, the second weightvalue, and context data of the event. Different types of machinelearning algorithms include, without limitation, decision treealgorithm, random forest algorithm, graph neural network algorithm,matrix factorization algorithm, logistic regression algorithm, orscalable vector machines algorithm.

In various embodiments, the context data of the event is determinedbased on content data of the event, or content data of the media asset.In various embodiments, the cultural distance prediction system maycause the display of a cultural distance score of the event on a userinterface of a client device. In various embodiments, for a singleidentified event from the media asset, the cultural distance predictionsystem may calculate cultural distance scores for each category ofcultural attributes, including cultural artifacts, life milestones,interpersonal dynamics, social influence, moral values, protectionism,global standing, etc. The cultural distance prediction system maygenerate a composite score for the event based on a median or averagescore of all the scores calculated for each category. In variousembodiments, as soon as a composite score is generated for eachidentified event, scene, theme, and genre, the cultural distanceprediction system may generate an overall predicted cultural distancescore for the media asset. In various embodiments, the cultural distanceprediction system may determine a level of predicted appeal of the eventby an audience of the culture of destination based on a value of thecultural distance score.

For some embodiments, a distance scale is utilized to indicate the levelof cultural distance or a level of predicted appeal of a media asset, oran event, scene, theme, trope, genre, or a subgenre associatedtherewith, between two geographical regions. The distance scaleindicates levels, including congruent (0-20), complementary (21-40),convergent (41-60), conflicting (61-80), and combustible (81-100). Thegreater value indicates the greater difference between cultures. Thecultural distance prediction system may cause the display of scores anddistance scales on a user interface of a client device, as shown in FIG.8 , as an example.

Various embodiments enable classification and subclassification ofcultural attributes based on classes and subclasses defined by apredetermined cultural attributes classification ontology or taxonomy. Acultural attribute category may represent a class or a subclass in thecultural attribute classification ontology/taxonomy. A culturalattribute category may include cultural attributes that are specific toa culture of a geographical region. In various embodiments, the classesof the cultural attribute classification ontology/taxonomy may includecultural artifacts, life milestones, interpersonal dynamics, socialinfluence, moral values, protectionism, and global standing, asillustrated in FIG. 5 .

In various embodiments, the cultural attribute prediction system maydetermine the most relevant class and subclass in the cultural attributeclassification ontology/taxonomy based on context data of the event orthe context data of the media asset.

As used herein, a media asset can comprise a video asset, a videocontent item, an audio asset, or an audio content item. As used herein,a cultural distance score can comprise a numerical value, which may ormay not be selected on a scale. Likewise, a feedback score can comprisea numerical value, which may or may not be selected on a scale. As usedherein, an event can comprise an audio content element (e.g., music,background noise, etc.), a visual content element (e.g., video, visualeffects, colors, etc.), a textual content element (e.g., subtitles), aspeech content element (e.g., dialog during an event or over a scene),or some combination thereof, that occurs within (e.g., is presented by)content of a media asset at a particular point on a timeline (e.g., aparticular timestamp or timecode) of the media asset. For instance, agiven event can comprise one or more of noise generated, music played,items displayed, actions or activity displayed by an actor, or wordsspoken. As used herein, an emotional event can refer to an event thathas the possibility of invoking an emotional response in an audiencemember who observes or experiences the event.

As used herein, a scene can comprise multiple events that occur within(e.g., are presented by) content of a media asset over a duration of atimeline (e.g., a range of timestamps or timecodes) of the media asset.As used herein, timestamp and timecode are used interchangeably.

As used herein, a machine-learning (ML) model can comprise anypredictive model that is generated based on (or that is trained on)training data. Once generated/trained, a machine-learning model canreceive one or more inputs (e.g., one or more features) and generate anoutput for the inputs based on the model’s training. Different types ofmachine-learning models include, without limitation, ones trained usingsupervised learning, unsupervised learning, reinforcement learning, ordeep learning (e.g., complex neural networks).

Reference will now be made in detail to embodiments, examples of whichare illustrated in the appended drawings. The present disclosure may,however, be embodied in many different forms and should not be construedas being limited to the embodiments set forth herein.

FIG. 1 is a block diagram showing an example data system 100 thatincludes a cultural distance prediction system for media assets(hereafter, the cultural distance prediction system 122), according tovarious embodiments. By including the cultural distance predictionsystem 122, the data system 100 can facilitate cultural distanceprediction of a media asset as described herein, which in turn canenable the generation of cultural distance prediction scores for thecontent, including the associated events and scenes, and scores for thecontent associated characteristics, including themes, tropes, andgenres. In particular, a user at the client device 102 can access thecultural distance prediction system 122 (e.g., via a graphical userinterface presented on a software application on the client device 102)and use the cultural distance prediction system 122 to generate culturaldistance prediction scores for a media asset (e.g., video asset or audioasset) selected by the user.

As shown, the data system 100 includes one or more client devices 102, aserver system 108, and a network 106 (e.g., including Internet,wide-area-network (WAN), local-area-network (LAN), wireless network,etc.) that communicatively couples them together. Each client device 102can host a number of applications, including a client softwareapplication 104. The client software application 104 can communicatedata with the server system 108 via a network 106. Accordingly, theclient software application 104 can communicate and exchange data withthe server system 108 via network 106.

The server system 108 provides server-side functionality via the network106 to the client software application 104. While certain functions ofthe data system 100 are described herein as being performed by thecultural distance prediction system 122 on the server system 108, itwill be appreciated that the location of certain functionality withinthe server system 108 is a design choice. For example, it may betechnically preferable to initially deploy certain technology andfunctionality within the server system 108, but to later migrate thistechnology and functionality to the client software application 104where the client device 102 provides cultural distance predictionoperations, such as determining (e.g., identifying) events in a mediaasset, determining (e.g., identifying) geographical region correspondingto the culture of origin and the culture of destination, receiving(e.g., accessing) weight values of cultural attribute categoriesrespectively associated with the geographical regions of the culture oforigin and the culture of destination, generating cultural distancescore of events, and causing display of the cultural distance score on auser interface of the client device 102

The server system 108 supports various services and operations that areprovided to the client software application 104 by the cultural distanceprediction system 122. Such operations include transmitting data fromthe cultural distance prediction system 122 to the client softwareapplication 104, receiving data from the client software application 104to the cultural distance prediction system 122, and the culturaldistance prediction system 122 processing data generated by the clientsoftware application 104. Data exchanges within the data system 100 maybe invoked and controlled through operations of software componentenvironments available via one or more endpoints, or functions availablevia one or more user interfaces of the client software application 104,which may include web-based user interfaces provided by the serversystem 108 for presentation at the client device 102.

With respect to the server system 108, each of an Application ProgramInterface (API) server 110 and a web server 112 is coupled to anapplication server 116, which hosts the cultural distance predictionsystem 122. The application server 116 is communicatively coupled to adatabase server 118, which facilitates access to a database 120 thatstores data associated with the application server 116, including datathat may be generated or used by the cultural distance prediction system122.

The API server 110 receives and transmits data (e.g., API calls,commands, requests, responses, and authentication data) between theclient device 102 and the application server 116. Specifically, the APIserver 110 provides a set of interfaces (e.g., routines and protocols)that can be called or queried by the client software application 104 inorder to invoke the functionality of the application server 116. The APIserver 110 exposes various functions supported by the application server116 including, without limitation: user registration; loginfunctionality; data object operations (e.g., generating, storing,retrieving, encrypting, decrypting, transferring, access rights,licensing, etc.); and user communications.

Through one or more web-based interfaces (e.g., web-based userinterfaces), the web server 112 can support various functionality of thecultural distance prediction system 122 of the application server 116including, without limitation: screening content of a media asset; oridentifying cultures of origin and destination, accessing weight valuesof the cultural attribute category, generating cultural distance scoreusing machine learning algorithms.

The application server 116 hosts a number of applications andsubsystems, including the cultural distance prediction system 122, whichsupports various functions and services with respect to variousembodiments described herein.

The application server 116 is communicatively coupled to a databaseserver 118, which facilitates access to database(s) 120 in which may bestored data associated with the cultural distance prediction system 122.Data associated with the cultural distance prediction system 122 caninclude, without limitation, data describing one or more eventsidentified in content of a media asset, one or more event classificationlabels identified for events, one or more event subclassification labelsidentified for events, one or more scenes identified in the content ofthe media asset, one or more scene attributes for scenes, one or morethemes identified in the content of the media asset, and one or moretitle-level attributes (hereafter, title attributes) for the mediaasset.

FIG. 2 is a block diagram illustrating an example cultural distanceprediction system 200 for media assets, according to variousembodiments. For some embodiments, the cultural distance predictionsystem 200 represents an example of the cultural distance predictionsystem 100 described with respect to FIG. 1 . As shown, the culturaldistance prediction system 200 comprises an event analyzer 210, a sceneanalyzer 220, a theme analyzer 230, a genre analyzer 240, a culturalattribute analyzer 250, a time-based and knowledge-based elementanalyzer 255, a cultural distance score generator 260, and a database270. According to various embodiments, one or more of the event analyzer210, scene analyzer 220, theme analyzer 230, genre analyzer 240,cultural attribute analyzer 250, time-based and knowledge-based elementanalyzer 255, and cultural distance score generator 260 are implementedby one or more hardware processors 202. Data (e.g., contextual data fora media asset) generated by one or more of the event analyzer 210, thescene analyzer 220, the theme analyzer 230, the genre analyzer 240, thecultural attribute analyzer 250, the time-based and knowledge-basedelement analyzer 255, and the cultural distance score generator 260 maybe stored in the database 270 of the cultural distance prediction system200.

The event analyzer 210 is configured to determine (e.g., identify) oneor more events within content data of a media asset by the culturaldistance prediction system 200. For some embodiments, the event analyzer210 determines at least one event (of the one or more events) within thecontent data by scanning the content data for events that relate to atleast one event classification and automatically identifies at least oneevent in the content data. Alternatively, or in addition, for someembodiments, the event analyzer 210 determines at least one event (ofthe one or more events) within the content data by receiving one or moreuser selections of at least one of the one or more events within thecontent data. For example, a graphical user interface can be displayedat a client device (e.g., 102), where the graphical user interface canenable a user (e.g., human reviewer, system administrator) at the clientdevice to screen the content data of the media asset (e.g., via a panelor window with a timeline and content player controls). As the userscreens the content data through the graphical user interface, the usercan submit one or more user inputs to identify an occurrence of an eventat a particular timestamp (or timecode). In various embodiments, theevent analyzer 210 may determine (e.g., identify) an event based on aset of signals provided by at least one computer vision analysis (e.g.,video, visual effects, colors, etc.), audio analysis (e.g.,speech/dialog, music, background noise), or natural language processing(NLP) of content (e.g., subtitles) presented by the current media asset.For instance, the event analyzer 210 can determine an event beingrelevant to cultural attributes of religious practice.

The scene analyzer 220 is configured to determine (e.g., identify) oneor more scenes within content data of a media asset, where each scenecomprises one or more events (e.g., a plurality of events determined bythe event analyzer 210). Additionally, scene analyzer 220 can beconfigured to determine (e.g., identify) one or more scene attributesfor at least one of the one or more scenes. For various embodiments, agiven scene attribute for a given scene can be determined based onevents that compose the scene. For instance, one or more sceneattributes for a given scene can be based on at least one of: one ormore events of the given scene; one or more event classification labelsfor the one or more events; or one or more event subclassificationlabels for the one or more events. Examples of scene attributes caninclude, without limitation: a frequency of events within a given scene;a mixture of events of different event classification labels within thegiven scene; a distance between two events within the given scene; aduration of the given scene; whether the scene is direct, explicit,implied; and an aftermath. In various embodiments, the scene analyzer220 determines (e.g., identifies) at least one scene within the contentdata, at least one scene attribute, or both by receiving one or moreuser selections of at least one scene or scene attribute.

The theme analyzer 230 is configured to determine (e.g., identify) oneor more themes of a media asset based on at least one or more events,scenes, or scene attributes. For various embodiments, a given theme canbe determined based on at least one of the set of scenes or the set ofscene attributes. Examples of themes can include, without limitation,theme type (e.g., coming of age context, good versus evil context,etc.), and attributes that explain dimensions of the theme (e.g., doesthe theme involve the main character, is the theme imitable, is thetheme reproducible). For some embodiments, the theme analyzer 230determines (e.g., identifies) at least one theme for the media asset byreceiving one or more user selections of at least one theme. Forexample, a graphical user interface can be displayed at a client device(e.g., 102), where the graphical user interface can enable a user (e.g.,human reviewer) at the client device to identify one or more themes forthe media asset. In various embodiments, the theme analyzer 230 is alsoconfigured to determine one or more tropes of media assets based on atleast one or more events, scenes, or themes

The genre analyzer 240 is configured to determine (e.g., identify) oneor more genre or subgenre of media assets based on at least one or moreone or more events, scenes, themes, or tropes. Genre is a category ofmedia assets determined based on a set of stylistic criteria. Examplesof genre may include, action, adventure, comedy, crime and mystery,fantasy, historical, horror, romance, satire, science fiction,speculative, thriller, etc. A subgenre may be a subordinate within agenre. Two events or scenes within a media asset being the same genremay still sometimes differ in subgenre. For example, if a comedy scenehas darker and more frightening elements of comedy, it would belong inthe subgenre of dark comedy. As another example, a comedy story thatfeatures a romantic story would belong to the subgenre of a romanticcomedy. A graphical user interface can be displayed at a client device(e.g., 102), where the graphical user interface can enable a user (e.g.,human reviewer, system administrator) at the client device to identifyone or more genres or subgenres for the media asset.

The cultural attribute analyzer 250 is configured to determine one ormore cultural attribute that is relevant to one or more events, scenes,themes, tropes, genres and subgenres. The cultural attribute analyzermay generate a customized cultural attribute graph for each identifiedevent, scene, theme, trope, genre, and subgenre based on thegeographical region relevant to the media asset and one or more culturalattributes available in the multi-dimensional hierarchy of culturalattributes. The multi-dimensional hierarchy of cultural attributesrepresents the classes of the cultural attribute classificationontology/taxonomy, as illustrated in FIG. 5 . The customized culturalattribute graph may be stored in database 270.

The time-based and knowledge-based element analyzer 255 is configured todetermine time-based elements and determine knowledge-based (e.g.,geographical knowledge-based) elements that are relevant to eachidentified event, scene, theme, trope, genre, and subgenre. Time-basedelements include information associated with the production year,original release year, consumption date, or time period of the contentcreated for the media asset. For some embodiments, knowledge-basedelements may be determined based on metadata associated with the mediaasset. The knowledge-based elements include current policies, laws, ornews events associated with the geographical region of the culture ofdestination. The knowledge-based elements and time-based elements may beupdated periodically by the cultural distance prediction system.

The cultural distance score generator 260 is configured to use a machinelearning algorithm to generate cultural distance scores for one or moreevents, scenes, themes, tropes, genres, and subgenres based on thegeographical region and one or more cultural attributes. For someembodiments, the cultural distance score generator 260 comprises amachine-learning model that is trained to automatically determine (e.g.,identify) a cultural distance score for each event, scene, theme, trope,genre, and subgenre based on each customized cultural attribute graphgenerated by cultural attribute analyzer 250. Depending on theembodiment, the machine-learning model of the cultural distance scoregenerator 260 can be trained on data previously generated duringcultural distance prediction of another media asset (e.g, by thecultural distance prediction system 200). For some embodiments, themachine learning algorithm may be at least one of the decision treealgorithm(s), random forest algorithm, graph neural network (GNN)algorithm, or matrix factorization algorithm.

For some embodiments, the cultural distance score may be generated basedon user profiles available to the cultural distance prediction system200. A user profile may include data such as a user’s beliefs, values,and customs, represented by classes of the cultural attributeclassification ontology/taxonomy, including cultural artifacts, lifemilestones, interpersonal dynamics, social influence, moral values,protectionism, and global standing, as illustrated in FIG. 5 . Thecultural distance score system may adjust the weight value of the mostrelevant cultural attribute associated with the identified events,scenes, themes, tropes, genres, and subgenres based on the user profile,so that the cultural distance score may be more accurately reflective ofthe particular user’s identity and beliefs.

FIG. 3 is a flowchart illustrating data flow within an example culturaldistance prediction system 300 for media assets during operation,according to various embodiments. As shown, the cultural distanceprediction system 300 comprises an event analyzer 310, a scene analyzer320, a theme analyzer 330, a genre analyzer 340, a cultural attributeanalyzer 350, a time-based and knowledge-based element analyzer 355, acultural distance score generator 360, and a database 370. For someembodiments, the event analyzer 310, the scene analyzer 320, the themeanalyzer 330, the genre analyzer 340, the cultural attribute analyzer350, a time-based and knowledge-based element analyzer 355, and thecultural distance score generator 360 are respectively similar to theevent analyzer 210, scene analyzer 220, theme analyzer 230, genreanalyzer 240, cultural attribute analyzer 250, a time-based andknowledge-based element analyzer 255, and cultural distance scoregenerator 260 of the cultural distance prediction system 200 of FIG. 2 .Additionally, each of the event analyzer 310, the scene analyzer 320,the theme analyzer 330, the genre analyzer 340, the cultural attributeanalyzer 350, a time-based and knowledge-based element analyzer 355, andthe cultural distance score generator 360 can comprise amachine-learning (ML) model that enables or facilitates operation asdescribed herein.

During operations, a media asset 302 (e.g., video media asset) isreceived and processed respectively by the event analyzer 310, the sceneanalyzer 320, the theme analyzer 330, the genre analyzer 340, and thecultural attribute analyzer 350, for each event, scene, theme and trope,and genre and subgenre. One or more events, scenes, themes and tropes,and genres and subgenres may also be determined (e.g., identified) byone or more user inputs from a user 304 (e.g., system administrator or auser of a client device).

The event analyzer 310, scene analyzer 320, theme analyzer 330, genreanalyzer 340, cultural attribute analyzer 350, and time-based andknowledge-based element analyzer 355, respectively output results to thecultural distance score generator 360 to generate scores for eachidentified event, scene, theme and trope, and genre and subgenre. Thecultural distance score generator 360 is configured to generate culturaldistance scores for one or more events, scenes, themes, tropes, genres,and subgenres based on pre-determined weight values of each culturalattribute or cultural attribute category that is applicable to eachevent, scene, theme, trope, genre, and subgenre. Depending on theembodiment, the machine-learning model of the cultural distance scoregenerator 660 may be trained on data previously generated duringcultural distance prediction of another media asset (e.g., by thecultural distance prediction system 200), and may also be trained basedon one or more user inputs from the user 304 (e.g., system administratoror a user of a client device).

Cultural distance score for the media asset 302 is generated and storedon the database 370. For some embodiments, the cultural distance scoresfor the media asset 302 may be generated based on a customized culturalattribute graph comprising cultural attributes (associated withpre-determined weight values) identified by cultural attribute analyzer350 and based on one of the following: one or more events identified byevent analyzer 310, one or more scenes identified by scene analyzer 320,one or more themes or tropes identified by theme analyzer 330, one ormore genres or subgenres identified by genre analyzer 340,

This cultural distance score for the media asset 302 on the database 370can be subsequently used by one or more review tools 390, via theassistance of media utilities transformer 380, for further downstreamanalysis of the media asset 302. For example, using the stored culturaldistance score, the review tools 390 can analyze the media asset 302 to:classify content of the media asset 302 for one or more cultures;generate content advisory for the media asset 302; generate a trailerfor the media asset 302; generate a title for the media asset 302;perform compliance review on the media asset 302; determine audiencesegments for the media asset 302; target the media asset 302 for anaudience; enable skipping of scenes in the media asset 302; filtercontent of the media asset 302; or predict cultural appeal or aversionof the media asset 302 with respect to specified culture, country,region, or the like. Each of the above-mentioned downstream analysiscorresponds to a utility, including content classification, artworkextraction and modification, content advisory generation, trailergeneration, compliance editing, video highlight generation, sceneskipping, genre detection, content filtering, culturalrelevancy/prediction/distance calculation, deep metadata analysis,culturalized (culture-based) listing generation, title “DNA” analysis,and audience segmentation and targeting.

Depending on the embodiment, content classification can comprise anautomated method in which technology is used to screen content of themedia asset 302 and automatically generate rating values for the mediaasset 302 for local markets worldwide. In general, contentclassification involves screening a film or television show forelements, such as violence, sexuality, or drugs, to determine itssuitability for viewers by age group in a specific local market. Ageratings, also known as maturity ratings, can provide the public with theinformation they need to make informed viewing decisions, as well asprotect children from viewing content that may be harmful to them.

Artwork extraction and modification can comprise an automated method inwhich artwork is extracted from content of the media asset 302, wherethe artwork selected for extraction is relevant for a promotionalutility (e.g., for enhanced click-through rates for the media asset 302on an digital store that presents the artwork in connection with themedia asset 302). In general, media content artwork can comprise adigital poster that is used to promote and advertise content and that isdesigned to persuade viewers to select content of a media asset.

Content advisory generation can comprise an automated method in whichtechnology is used to generate local content advisories for the mediaasset 302 accurately and consistently. In general, content advisories,also known as content warnings, can provide detailed information aboutthe types of objectionable content contained in film and television,such as violence, profanity, or drugs.

Trailer generation can comprise an automated method in which a traileris generated using artificial intelligence and machine-learningtechnology that indexes and packages the most relevant scenes of contentof the media asset 302. In general, a trailer can comprise a commercialadvertisement of a video content that is planned for exhibition -usually for films and television shows.

Compliance editing can comprise an automated method in which technologyis used to recommend the respective time-code ranges of non-compliantscenes within the media asset 302 for local markets worldwide. Ingeneral, content exhibition in local markets can be regulated to ensurecompliance with content classification and censorship laws. When aparticular content fails to comply with local policies, regulators canrequire that objectionable scenes be edited accordingly.

Video highlight generation can comprise an automated method for indexingthe most relevant scenes of video content of the media asset 302 andgenerating one or more short video clips from these scenes such that theshort video clips appeal to one or more cultural sensitivities or avalue system of a local audience (e.g., for the purpose of marketing andpromoting the content of the media asset 302).

Scene skipping can comprise an automated method in which time-basedmarkers (e.g., time-code ranges) of the media asset 302 that representthe duration of the objectionable scene are captured and provided to avideo/streaming platform, which can enable a feature “Skip Scene” (e.g.,a graphical user interface button on the client interface that a viewercan click to skip an objectionable scene).

Genre detection can comprise an automated method for detecting a genreof content of the media asset 302, which can be used for dynamic listingor content promotion/recommendation activities for the media asset 302.In general, genres and sub-genres for media content are categories thatdefine the content based on one or more of its narrative themes

Content filtering can comprise an automated method in which content(e.g., such as the content of the media asset 302) is suggested aparticular viewer at a given time. In general, the filtered content canbe displayed in the catalog (e.g., virtual shelves and trays) of adigital streaming platform to persuade a viewer to watch the content.

Cultural relevancy/prediction/distance calculation can comprise anautomated method in which a cultural distance is measured between two ormore cultures for the purposes of adapting content of the media asset302 to improve its appeal (e.g., relatability or suitability) withrespect to a local audience or to predict its appeal (e.g., relatabilityor suitability) with respect to the local audience, which can determinethe overall success of the media asset 302 in connection with that localaudience. The method can consider local laws, customs, or tastes andpreferences of the viewing audience in measuring this distance.

Deep metadata analysis can comprise an automated method in whichtechnology is used to generate, for content of the media asset 302,attributes at relevant time-code ranges that describe the content’smood, theme, time period, location, event, objectionable content,character, or another element that is important for enhanced search anddiscovery. In general, deep metadata regarding content of a media assetcan provide definitions that organize content to make it more visiblefor search engines and streaming platforms.

Culturalized (culture-based) listing generation can comprise anautomated method in which a listing of media assets (and the content ofeach listing element, such as title metadata, artwork, trailer, etc.) isgenerated based on localization and culturalization. In general,localization can comprise adapting content to a viewer’s geographicalenvironment in accordance with language, legal and technologyrequirements, while culturalization can comprise adapting content of themedia asset 302 to a user’s cultural environment (e.g., beliefs, values,and customs).

Title “DNA” analysis can comprise an automated method for generating acore set of instructions that dictate the essence of the content of themedia asset 302, which can be used to modify or generate (the context orstructure for) title metadata of the media asset 302. In general, titlemetadata for a media asset can be used to organize, index, analyze,manage, and service the media asset for enhanced distribution andconsumption.

Audience segmentation and targeting can comprise an automated method inwhich consumers can be profiled according to demographics,psychographics, gender, age, ethnicity, or other parameters, andconsumers within a target audience or cohort can be identified (e.g.,for enhanced marketing promotions and conversions) for the media asset302.

Dynamic advertising slot (or break) generation can comprise an automatedmethod in which a scene graph for the media asset 302 is generated,where the scene graph can provide details on emotional highs and lows incontent (e.g., video content) of the media asset 302 and createtime-based markers (aka time-code ranges) corresponding to the peakemotional events in the content of the media asset 302 The time-basedmarkers, along with other deep metadata, can be provided to avideo/streaming platform for optimal placement of advertisements for themedia asset 302. Dynamic advertising slot/break generation can ensurethat advertisements are placed dynamically in content of a media assetat moments that would incur the greatest impact.

FIGS. 4A-4B are flowcharts illustrating an example method 400 forcultural distance prediction of media assets, according to variousembodiments. The operations of method 400 illustrate the generation ofcultural distance scores for an identified event based on content dataof a media asset. The same or similar operations of a method may beutilized to generate cultural distance scores for an identified scene,theme, trope, genre, and subgenre of a media asset.

For example, methods 400 can be performed by the cultural distanceprediction system 122 described with respect to FIG. 1 , the culturaldistance prediction system 200 described with respect to FIG. 2 , thecultural distance prediction system described with respect to FIG. 3 ,or individual components thereof. An operation of various methodsdescribed herein may be performed by one or more hardware processors(e.g., central processing units or graphics processing units) of acomputing device (e.g., a desktop, server, laptop, mobile phone, tablet,etc.), which may be part of a computing system based on a cloudarchitecture Example methods described herein may also be implemented inthe form of executable instructions stored on a machine-readable mediumor in the form of electronic circuitry. For instance, the operations ofmethod 400 may be represented by executable instructions that, whenexecuted by a processor of a computing device, cause the computingdevice to perform the method 400. Depending on the embodiment, anoperation of an example method described herein may be repeated indifferent ways or involve intervening operations not shown. Though theoperations of example methods may be depicted and described in a certainorder, the order in which the operations are performed may vary amongembodiments, including performing certain operations in parallel.

At operation 402, content data for a media asset is accessed by (e.g.,using) a processor (e.g., hardware processor operating the culturaldistance prediction system 200). The media asset accessed can be oneselected for analysis or review (e.g., media asset review). Forinstance, the media asset can be selected by a user at a client device,such as by way of one or more user inputs to a graphical user interfacepresented by a standalone software application or a web browser softwareapplication. As described herein, examples of a media asset can include,without limitation, audio assets (e.g., music tracks, music albums,etc.) and video assets (e.g., motion pictures, feature films, televisionepisodes, etc.). Depending on the embodiment, the content data can beprovided by a media file or datastore associated with the media asset.

At operation 404, the processor may determine an event at a timestampwithin the content data of the media asset. The event corresponds to acultural attribute category. A cultural attribute category may representa class or a subclass in the cultural attribute classificationontology/taxonomy. A cultural attribute category may include culturalattributes that are specific to a culture of a geographical region. Invarious embodiments, the classes of the cultural attributeclassification ontology/taxonomy may include cultural artifacts, lifemilestones, interpersonal dynamics, social influence, moral values,protectionism, and global standing, as illustrated in FIG. 5 . Eachclass or category of the cultural attribute classificationontology/taxonomy may include one or more subclasses. For example,subclasses of cultural artifacts may include material, symbolic, andsacred; subclasses of life milestones may include birth, gainingindependence, falling in love, marriage, higher education, securing ajob, buying real property, experiencing loss, and death; subclasses ofinterpersonal dynamics may include egalitarianism, patriarchy, power,gender, and communication; subclasses of social influence may includesocietal norms, peer pressure, imitable behavior, social trust, andthreats; subclasses of moral values may include rituals, religion,parenting, child development, and institutions; subclasses ofprotectionism may include government, politics, military, and history;subclasses of global standing may include geolocation, language,economy, regulatory constraints, technology, and environment. In variousembodiments, the cultural attribute prediction system may determine themost relevant class and subclass in the cultural attributeclassification ontology/taxonomy based on context data of the event orthe context data of the media asset.

At operation 406, the processor identifies a geographical region (e.g.,a first geographical region) that corresponds to a culture of origin,and identifies a geographical region (e.g., a second geographicalregion) that corresponds to a culture of destination. For example, whenthe selected media asset is a movie produced in the United States, thefirst geographical region may be the United States, where the mediaasset was created. The second geographical region may be Japan, wherethe media asset is scheduled to be distributed or released. The cultureof origin is determined to be the culture of the United States, and theculture of destination may be Japanese culture.

At operation 408, the processor accesses a weight value (e.g., a firstweight value) that corresponds to a cultural attribute categoryassociated with the geographical region (eg., the first geographicalregion) of the culture of origin. The cultural attribute category may beassociated with a class or a subclass in the cultural attributeclassification ontology/taxonomy that is determined to be most relevantto the event. The determination of relevancy may be based on analyzingthe content or context data of the event or the content or context dataof the media asset as a whole.

At operation 410, the process accesses a weight value (e.g., a secondweight value) that corresponds to the cultural attribute categoryassociated with the geographical region (e.g., the second geographicalregion) of the culture of destination.

At operation 412, the processor uses a machine learning algorithm togenerates a cultural distance score of the event based on the weightvalue (e.g., a first weight value) corresponds to a cultural attributecategory associated with the geographical region (e.g., the firstgeographical region) of the culture of origin, on the weight value(e.g., a second weight value) corresponds to the cultural attributecategory associated with the geographical region (e.g., the secondgeographical region) of the culture of destination, and on the contextdata of the event or media asset. For some embodiments, the machinelearning algorithm may be at least one of the decision tree algorithm,random forest algorithm, graph neural network (GNN) algorithm, or matrixfactorization algorithm.

For some embodiments, weight values of cultural attribute categories andthe associated cultural attributes are pre-determined absolute valuesthat are accessible by the cultural distance prediction system 200. Theweight values may be updated based on time metadata and knowledge graph.Time metadata may include information associated with time-basedelements, including production year, original release year, consumptiondate, time period of the content of the media asset. Knowledge graph mayinclude information associated with geographical knowledge-basedelements, such as local laws, news events, and any other informationrelated to the current cultural and legal compliance environment of aparticular geographical region. For some embodiment, a law change in ageographical region will cause an update of the knowledge graphassociated with the region, which in turn causes a change to thepre-determined weight value of a cultural attribute associated with theregion. The detection of changes to the time metadata and knowledgegraph may be performed by a human reviewer or by the cultural distanceprediction system automatically.

For some embodiments, the processor may access metadata associated withthe media asset. The metadata includes time-based elements andgeographical knowledge-based elements. Time-based elements includeinformation associated with the production year, original release year,consumption date, or time period of the content created for the mediaasset. For some embodiments, the processor may access geographicalknowledge-based elements based on metadata associated with the mediaasset. The geographical knowledge-based elements include currentpolicies, laws, or news events associated with the geographical regionof the culture of destination. The processor may adjust the culturaldistance score based on the time-based elements and knowledge-basedelements. The time-based elements and knowledge-based elements may beupdated periodically by the processor.

At operation 414, the processor causes a display of the culturaldistance score on a user interface of a client device 104. For someembodiments, after operation 414, the operations of method 400 continuewith operation 416, as illustrated in FIG. 4B.

At operation 416, the processor identifies a weight value (e.g., a thirdweight value) based on the context data of the event. The context orcontextual data describes content of the event. For some embodiments,contextual data of an event or a media asset is generated based on atleast one of the events, scenes, themes, tropes, genres, or subgenresidentified for the media asset. The third weight value may be a culturalattribute (category) that corresponds to a subclass or subcategory ofthe cultural attribute category as described in operations 404-410. Thethird weight value of the cultural attribute (category) is associatedwith the second geographical region and may be determined based on acustomized cultural attribute graph (e.g., such as the culturalattribute graph as illustrated in FIG. 7 ) generated for the event basedon cultural attributes relevant to the second geographical region. Forsome embodiments, the cultural attribute (category) associated with thethird weight value, as determined based on context data of the event, isa more relevant cultural attribute (category) to the event compared tothe culture attribute category as described in operations 404-410.

At operation 418, the processor applies the weight value (e.g., firstweight value) corresponds to a cultural attribute category associatedwith the geographical region of the culture of origin and the weightvalue (e.g., second weight value) to the third weight value to generatethe cultural distance score for the event. For some embodiments,operation 418 may be performed via any one of the machine learningalgorithms as described herein.

At operation 420, after the cultural distance score is caused to bedisplayed on the user interface on the client device 102, the processmay receive a feedback score from the client device 102 or from a systemadministrator of the cultural distance prediction system 200

At operation 422, the processor determines a difference between thefeedback score and the cultural distance score. The difference may berepresented by a difference in the values of such scores. A largerdifference indicates a larger possibility that the cultural distancescore deviates from a score expected to be generated. For someembodiments, the differences between the weight value (e.g., firstweight value) corresponds to the cultural attribute category of theculture of origin and the weight value (e.g., second weight value)corresponds to the cultural attribute category of the culture ofdestination indicates a scaled range of a cultural distance score for aparticular event, scene, theme, trope, genre, and subgenre.

At operation 424, the process adjusts the machine learning algorithmbased on the difference. For some embodiments, a machine learningalgorithm may be adjusted by determining a weight value (e.g., thirdweight value) corresponding to a cultural attribute (category) that ismore relevant or appropriate based on context data of the event. Forexample, when an identified event relates to a person demolishes areligious statute, depending on the context data of such event, culturalattribute (category) “religious practice” may be a more relevantattribute compared to “religion,” or “religious festival.” Since eachcultural attribute (category) may be associated with a differentpre-determined absolute weight value. Identification of the mostrelevant attribute (category) associated with each event, scene, theme,trope, genre, and subgenre may affect the accuracy of cultural distanceprediction Cultural attribute and cultural attribute category are usedinterchangeably as a cultural attribute under a cultural attributecategory may itself be a category that comprises multiple attributes andor attribute categories.

FIG. 5 provides a block diagram illustrating example cultural attributecategories in a multi-dimensional hierarchy of cultural attributes basedon a predetermined cultural attributes classification ontology/taxonomy,according to various embodiments. The cultural attributes classificationontology/taxonomy includes cultural attribute categories, asillustrated, such as cultural artifacts 510, life milestones 520,interpersonal dynamics 530, social influence 540, moral values 550,protectionism 560, and global standing 570. A customized culturalattribute graph may be generated for each identified event, scene,theme, genre, and subgenre based on the predetermined culturalattributes classification ontology/taxonomy.

Cultural artifacts 510 pertain to items created by humans, which givesinformation about the culture of its creator and users Subclasses ofCultural Artifacts may include material, symbolic, and sacred.

Life milestones 520 pertain to events that highlight importantachievements in a person’s life. Subclasses of life milestones mayinclude birth, gaining independence, falling in love, marriage, highereducation, securing a job, buying real property, experiencing loss, anddeath.

Interpersonal dynamics 530 pertain to interactions among members in aspecific social context and their treatment of one another. Subclassesof interpersonal dynamics may include egalitarianism, patriarchy, power,gender, and communication.

Social influence 540 pertains to ways in which individuals change theirbehavior to meet the demands of a social environment. Subclasses ofsocial influence may include societal norms, peer pressure, imitablebehavior, social trust, and threats.

Moral values 550 pertain to the system of beliefs that emerge from corevalues. Morals are specific and context-driven rules that govern aperson’s thoughts, emotions, actions, and behavior, such as integrity,honesty, helping others in need, valuing others’ time, etc. Subclassesof moral values may include rituals, religion, parenting, childdevelopment, and institutions.

Protectionism 560 pertains to policies of protecting domestic industriesand culture against foreign competition and culture mixing(contamination). Subclasses of protectionism may include government,politics, military, and history

Global standing 570 pertains to a country’s approval rating with respectto its reputation as perceived by other nations. Subclasses of globalstanding may include geolocation, language, economy, regulatoryconstraints, technology, and environment.

FIG. 6 illustrates an example set of weight values pre-determined forcultural attribute categories for each geographical region, according tovarious embodiments. For some embodiments, the weight value (e.g., thefirst weight value) of the cultural attribute category culturalartifacts 510 for geographical region 1 is pre-determined as 70 (e.g.,weight value 602). The weight value (e.g., the second weight value) ofthe same cultural attribute category cultural artifacts 510 forgeographical region 2 is pre-determined as 50 (e.g., weight value 604).For some embodiments, weight values of cultural attribute categories andthe associated cultural attributes are pre-determined absolute valuesthat are accessible by the cultural distance prediction system 200. Theweight values may be updated based on time metadata and knowledge graph,as described herein.

FIG. 7 illustrates an example customized cultural attribute graphgenerated based on an example event for an example geographical region,according to various embodiments. In FIG. 7 , a customized attributegraph 720 is generated for an identified event 714 “a person demolisheda religious statute,” for a geographical region corresponding to theculture of destination 704. The customized attribute graph 720 includesa cultural attribute category 510, a cultural attribute category 708, acultural attribute category 710, and a cultural attribute (category)712. The cultural attribute (category) 712 may comprise plural cultureattributes as end nodes, or may itself be a culture attribute as an endnode in the customized attribute graph 720. The cultural distanceprediction system 200 determines, based on context data of the event714, that the cultural attribute (category) 712 is the most relevant tothe event 714. The cultural distance prediction system 200 accesses theweight value 30 (e.g., the third weight value) of the cultural attribute(category) 712, and determines the cultural distance score inconjunction with the weight value 602 of the cultural attribute category510 corresponding to the culture of origin 716, and the weight value 604of the cultural attribute category 510 corresponding to the culture ofdestination 704. The cultural distance score for the event 714 isdetermined to be 10 (cultural distance score 702), as illustrated inFIG. 7 . The determination of cultural distance score may be based onany one of the machine learning algorithms, including withoutlimitation, decision tree algorithm, random forest algorithm, graphneural network algorithm, matrix factorization algorithm, logisticregression algorithm, or scalable vector machines algorithm.

FIG. 8 illustrates an example graphical user interface 800 showing anexample cultural distance score generated for a media asset and examplescores generated for an event, scene, theme, and genre of the mediaasset, according to various embodiments. The graphical user interface800 enables a user to view or edit one or more cultural distance scoresassociated with a media asset and the associated event, scene, theme,and genre. As illustrated in the graphical user interface 800, thecultural distance score 702 generated for the event 714 is 10. Theoverall cultural distance score generated for the media asset betweenthe culture of origin (C1) and the culture of destination (C2) is 19,corresponding to the “congruent” distance scale. A distance scale isutilized to indicate the level of cultural distance between twogeographical regions with respect to a media asset, or an event, scene,theme, trope, genre, or subgenre associated therewith. The distancescale indicates levels, such as congruent (0-20), complementary (21-40),convergent (41-60), conflicting (61-80), and combustible (81-100). Agreater value indicates a greater difference between cultures.

FIG. 9 is a block diagram illustrating an example of a softwarearchitecture 902 that may be installed on a machine, according to someexample embodiments. FIG. 9 is merely a non-limiting example of asoftware architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 902 may be executing onhardware such as a machine 1000 of FIG. 10 that includes, among otherthings, processors 1010, memory 1030, and input/output (I/O) components1050. A representative hardware layer 904 is illustrated and canrepresent, for example, the machine 1000 of FIG. 10 . The representativehardware layer 904 comprises one or more processing units 906 havingassociated executable instructions 908. The executable instructions 908represent the executable instructions of the software architecture 902.The hardware layer 904 also includes memory or storage modules 910,which also have the executable instructions 908. The hardware layer 904may also comprise other hardware 912, which represents any otherhardware of the hardware layer 904, such as the other hardwareillustrated as part of the machine 1000.

In the example architecture of FIG. 9 , the software architecture 902may be conceptualized as a stack of layers, where each layer providesparticular functionality. For example, the software architecture 902 mayinclude layers such as an operating system 914, libraries 916,frameworks/middleware 918, applications 920, and a presentation layer944. Operationally, the applications 920 or other components within thelayers may invoke API calls 924 through the software stack and receive aresponse, returned values, and so forth (illustrated as messages 926) inresponse to the API calls 924. The layers illustrated are representativein nature, and not all software architectures have all layers. Forexample, some mobile or special-purpose operating systems may notprovide a frameworks/middleware 918 layer, while others may provide sucha layer. Other software architectures may include additional ordifferent layers.

The operating system 914 may manage hardware resources and providecommon services. The operating system 914 may include, for example, akernel 928, services 930, and drivers 932. The kernel 928 may act as anabstraction layer between the hardware and the other software layers.For example, the kernel 928 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 930 may provideother common services for the other software layers. The drivers 932 maybe responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 932 may include display drivers,camera drivers, Bluetooth® drivers, flash memory drivers, serialcommunication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi®drivers, audio drivers, power management drivers, and so forth dependingon the hardware configuration.

The libraries 916 may provide a common infrastructure that may beutilized by the applications 920 and/or other components and/or layers.The libraries 916 typically provide functionality that allows othersoftware modules to perform tasks in an easier fashion than byinterfacing directly with the underlying operating system 914functionality (e.g., kernel 928, services 930, or drivers 932). Thelibraries 916 may include system libraries 934 (e.g., C standardlibrary) that may provide functions such as memory allocation functions,string manipulation functions, mathematic functions, and the like. Inaddition, the libraries 916 may include API libraries 936 such as medialibraries (e.g., libraries to support presentation and manipulation ofvarious media formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, andPNG), graphics libraries (e.g., an OpenGL framework that may be used torender 2D and 3D graphic content on a display), database libraries(e.g., SQLite that may provide various relational database functions),web libraries (e.g., WebKit that may provide web browsingfunctionality), and the like. The libraries 916 may also include a widevariety of other libraries 938 to provide many other APIs to theapplications 920 and other software components/modules.

The frameworks 918 (also sometimes referred to as middleware) mayprovide a higher-level common infrastructure that may be utilized by theapplications 920 or other software components/modules. For example, theframeworks 918 may provide various graphical user interface functions,high-level resource management, high-level location services, and soforth. The frameworks 918 may provide a broad spectrum of other APIsthat may be utilized by the applications 920 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system or platform.

The applications 920 include built-in applications 940 and/orthird-party applications 942. Examples of representative built-inapplications 940 may include, but are not limited to, a homeapplication, a contacts application, a browser application, a bookreader application, a location application, a media application, amessaging application, or a game application.

The third-party applications 942 may include any of the built-inapplications 940, as well as a broad assortment of other applications.In a specific example, the third-party applications 942 (e.g., anapplication developed using the Android™ or iOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform)may be mobile software running on a mobile operating system such asiOS™, Android™, or other mobile operating systems. In this example, thethird-party applications 942 may invoke the API calls 924 provided bythe mobile operating system such as the operating system 914 tofacilitate functionality described herein.

The applications 920 may utilize built-in operating system functions(e.g., kernel 928, services 930, or drivers 932), libraries (e.g.,system libraries 934, API libraries 936, and other libraries 938), orframeworks/middleware 918 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such asthe presentation layer 944. In these systems, the application/module“logic” can be separated from the aspects of the application/module thatinteract with the user.

Some software architectures utilize virtual machines. In the example ofFIG. 9 , this is illustrated by a virtual machine 948. The virtualmachine 948 creates a software environment where applications/modulescan execute as if they were executing on a hardware machine (e.g., themachine 1000 of FIG. 10 ). The virtual machine 948 is hosted by a hostoperating system (eg., the operating system 914) and typically, althoughnot always, has a virtual machine monitor 946, which manages theoperation of the virtual machine 948 as well as the interface with thehost operating system (e.g., the operating system 914). A softwarearchitecture executes within the virtual machine 948, such as anoperating system 950, libraries 952, frameworks/middleware 954,applications 956, or a presentation layer 958. These layers of softwarearchitecture executing within the virtual machine 948 can be the same ascorresponding layers previously described or may be different

FIG. 10 illustrates a diagrammatic representation of a machine 1000 inthe form of a computer system within which a set of instructions may beexecuted for causing the machine 1000 to perform any one or more of themethodologies discussed herein, according to an embodiment.Specifically, FIG. 10 shows a diagrammatic representation of the machine1000 in the example form of a computer system, within which instructions1016 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1000 to perform any oneor more of the methodologies discussed herein may be executed. Forexample, the instructions 1016 may cause the machine 1000 to execute themethod 400 described above with respect to FIGS. 4A-4B. The instructions1016 transform the general, non-programmed machine 1000 into aparticular machine 1000 programmed to carry out the described andillustrated functions in the manner described. In alternativeembodiments, the machine 1000 operates as a standalone device or may becoupled (e.g., networked) to other machines. In a networked deployment,the machine 1000 may operate in the capacity of a server machine or aclient machine in a server-client network environment, or as a peermachine in a peer-to-peer (or distributed) network environment Themachine 1000 may comprise, but not be limited to, a server computer, aclient computer, a personal computer (PC), a tablet computer, a laptopcomputer, a netbook, a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smart phone, amobile device, or any machine capable of executing the instructions1016, sequentially or otherwise, that specify actions to be taken by themachine 1000. Further, while only a single machine 1000 is illustrated,the term “machine” shall also be taken to include a collection ofmachines 1000 that individually or jointly execute the instructions 1016to perform any one or more of the methodologies discussed herein.

The machine 1000 may include processors 1010, memory 1030, and I/Ocomponents 1050, which may be configured to communicate with each othersuch as via a bus 1002. In an embodiment, the processors 1010 (e.g., ahardware processor, such as a central processing unit (CPU), a reducedinstruction set computing (RISC) processor, a complex instruction setcomputing (CISC) processor, a graphics processing unit (GPU), a digitalsignal processor (DSP), an application-specific integrated circuit(ASIC), a radio-frequency integrated circuit (RFIC), another processor,or any suitable combination thereof) may include, for example, aprocessor 1012 and a processor 1014 that may execute the instructions1016. The term “processor” is intended to include multi-core processorsthat may comprise two or more independent processors (sometimes referredto as “cores”) that may execute instructions contemporaneously. AlthoughFIG. 10 shows multiple processors 1010, the machine 1000 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiples cores, or any combinationthereof

The memory 1030 may include a main memory 1032, a static memory 1034,and a storage unit 1036 including machine-readable medium 1038, eachaccessible to the processors 1010 such as via the bus 1002. The mainmemory 1032, the static memory 1034, and the storage unit 1036 store theinstructions 1016 embodying any one or more of the methodologies orfunctions described herein. The instructions 1016 may also reside,completely or partially, within the main memory 1032, within the staticmemory 1034, within the storage unit 1036, within at least one of theprocessors 1010 (e.g., within the processor’s cache memory), or anysuitable combination thereof, during execution thereof by the machine1000.

The I/O components 1050 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1050 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components1050 may include many other components that are not shown in FIG. 10 .The I/O components 1050 are grouped according to functionality merelyfor simplifying the following discussion, and the grouping is in no waylimiting. In various embodiments, the I/O components 1050 may includeoutput components 1052 and input components 1054. The output components1052 may include visual components (e.g., a display such as a plasmadisplay panel (PDP), a light-emitting diode (LED) display, a liquidcrystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1054 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further embodiments, the I/O components 1050 may include biometriccomponents 1056, motion components 1058, environmental components 1060,or position components 1062, among a wide array of other components. Themotion components 1058 may include acceleration sensor components (eg.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope), and so forth. The environmental components1060 may include, for example, illumination sensor components (e.g.,photometer), temperature sensor components (e.g., one or morethermometers that detect ambient temperature), humidity sensorcomponents, pressure sensor components (e.g., barometer), acousticsensor components (e.g., one or more microphones that detect backgroundnoise), proximity sensor components (e.g., infrared sensors that detectnearby objects), gas sensors (e.g., gas detection sensors to detectconcentrations of hazardous gases for safety or to measure pollutants inthe atmosphere), or other components that may provide indications,measurements, or signals corresponding to a surrounding physicalenvironment. The position components 1062 may include location sensorcomponents (e.g., a Global Positioning System (GPS) receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologiesThe I/O components 1050 may include communication components 1064operable to couple the machine 1000 to a network 1080 or devices 1070via a coupling 1082 and a coupling 1072, respectively. For example, thecommunication components 1064 may include a network interface componentor another suitable device to interface with the network 1080. Infurther examples, the communication components 1064 may include wiredcommunication components, wireless communication components, cellularcommunication components, near field communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1070 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1064 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1064 may include radio frequency identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1064, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth

Certain embodiments are described herein as including logic or a numberof components, modules, elements, or mechanisms. Such modules canconstitute either software modules (e.g., code embodied on amachine-readable medium or in a transmission signal) or hardwaremodules. A “hardware module” is a tangible unit capable of performingcertain operations and can be configured or arranged in a certainphysical manner. In various example embodiments, one or more computersystems (e.g., a standalone computer system, a client computer system,or a server computer system) or one or more hardware modules of acomputer system (e.g., a processor or a group of processors) areconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware module is implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module can include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module can be a special-purpose processor, such as afield-programmable gate array (FPGA) or an ASIC. A hardware module mayalso include programmable logic or circuitry that is temporarilyconfigured by software to perform certain operations. For example, ahardware module can include software encompassed within ageneral-purpose processor or other programmable processor. It will beappreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) can bedriven by cost and time considerations.

Accordingly, the phrase “module” should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering embodiments in which hardwaremodules are temporarily configured (e.g, programmed), each of thehardware modules need not be configured or instantiated at any oneinstance in time. For example, where a hardware module comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Software canaccordingly configure a particular processor or processors, for example,to constitute a particular hardware module at one instance of time andto constitute a different hardware module at a different instance oftime.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules can be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications can be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between or among such hardware modulesmay be achieved, for example, through the storage and retrieval ofinformation in memory structures to which the multiple hardware moduleshave access. For example, one hardware module performs an operation andstores the output of that operation in a memory device to which it iscommunicatively coupled. A further hardware module can then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules can also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein can beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein can be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method can be performed by one or more processors orprocessor-implemented modules. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines 1000 including processors 1010),with these operations being accessible via a network (e.g., theInternet) and via one or more appropriate interfaces (e.g., an API). Incertain embodiments, for example, a client device may relay or operatein communication with cloud computing systems, and may access circuitdesign information in a cloud environment.

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine 1000, butdeployed across a number of machines 1000. In some example embodiments,the processors 1010 or processor-implemented modules are located in asingle geographic location (e.g., within a home environment, an officeenvironment, or a server farm). In other example embodiments, theprocessors or processor-implemented modules are distributed across anumber of geographic locations.

Executable Instructions and Machine Storage Medium

The various memories (i.e., 1030, 1032, 1034, and/or the memory of theprocessor(s) 1010) and/or the storage unit 1036 may store one or moresets of instructions 1016 and data structures (e.g., software) embodyingor utilized by any one or more of the methodologies or functionsdescribed herein. These instructions (e.g., the instructions 1016), whenexecuted by the processor(s) 1010, cause various operations to implementthe disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably. The terms refer to a single or multiple storagedevices and/or media (e.g., a centralized or distributed database,and/or associated caches and servers) that store executable instructions1016 and/or data. The terms shall accordingly be taken to include, butnot be limited to, solid-state memories, and optical and magnetic media,including memory internal or external to processors. Specific examplesof machine-storage media, computer-storage media and/or device-storagemedia include non-volatile memory, including by way of examplesemiconductor memory devices, e.g., erasable programmable read-onlymemory (EPROM), electrically erasable programmable read-only memory(EEPROM), FPGA, and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium” discussedbelow.

Transmission Medium

In various embodiments, one or more portions of the network 1080 may bean ad hoc network, an intranet, an extranet, a virtual private network(VPN), a LAN, a wireless LAN (WLAN), a WAN, a wireless WAN (WWAN), ametropolitan-area network (MAN), the Internet, a portion of theInternet, a portion of the public switched telephone network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, the network1080 or a portion of the network 1080 may include a wireless or cellularnetwork, and the coupling 1082 may be a Code Division Multiple Access(CDMA) connection, a Global System for Mobile communications (GSM)connection, or another type of cellular or wireless coupling. In thisexample, the coupling 1082 may implement any of a variety of types ofdata transfer technology, such as Single Carrier Radio TransmissionTechnology (1xRTT), Evolution-Data Optimized (EVDO) technology, GeneralPacket Radio Service (GPRS) technology, Enhanced Data rates for GSMEvolution (EDGE) technology, third Generation Partnership Project (3GPP)including 3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High-Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long-TermEvolution (LTE) standard, others defined by various standard-settingorganizations, other long-range protocols, or other data transfertechnology.

The instructions may be transmitted or received over the network using atransmission medium via a network interface device (e.g., a networkinterface component included in the communication components) andutilizing any one of a number of well-known transfer protocols (e.g.,hypertext transfer protocol (HTTP)). Similarly, the instructions may betransmitted or received using a transmission medium via the coupling(e.g., a peer-to-peer coupling) to the devices 1070. The terms“transmission medium” and “signal medium” mean the same thing and may beused interchangeably in this disclosure. The terms “transmission medium”and “signal medium” shall be taken to include any intangible medium thatis capable of storing, encoding, or carrying the instructions forexecution by the machine, and include digital or analog communicationssignals or other intangible media to facilitate communication of suchsoftware. Hence, the terms “transmission medium” and “signal medium”shall be taken to include any form of modulated data signal, carrierwave, and so forth. The term “modulated data signal” means a signal thathas one or more of its characteristics set or changed in such a manneras to encode information in the signal.

Computer-Readable Medium

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals. For instance, an embodiment described herein can be implementedusing a non-transitory medium (e.g., a non-transitory computer-readablemedium).

Throughout this specification, plural instances may implement resources,components, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. The terms “a” or “an” should be read as meaning “atleast one,” “one or more,” or the like. The presence of broadening wordsand phrases such as “one or more,” “at least,” “but not limited to,” orother like phrases in some instances shall not be read to mean that thenarrower case is intended or required in instances where such broadeningphrases may be absent. Additionally, boundaries between variousresources, operations, modules, engines, and data stores are somewhatarbitrary, and particular operations are illustrated in a context ofspecific illustrative configurations. Other allocations of functionalityare envisioned and may fall within a scope of various embodiments . Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

It will be understood that changes and modifications may be made to thedisclosed embodiments without departing from the scope . These and otherchanges or modifications are intended to be included within the scope.

1-20. (canceled)
 21. A method comprising: accessing, by a hardware processor, content data for a media asset; determining, by the hardware processor, an event at a timestamp within the content data of the media asset, the event being relevant to a cultural attribute category; identifying, by the hardware processor, a first geographical region corresponding to a culture of origin of the media asset and a second geographical region corresponding to a culture of destination of the media asset, the first geographical region being where content of the media asset was created, the second geographical region being where the media asset is targeted for release to an audience; accessing, by the hardware processor, a first weight value of the cultural attribute category associated with the first geographical region; accessing, by the hardware processor, a second weight value of the cultural attribute category associated with the second geographical region; using machine learning algorithm to generate, by the hardware processor, a cultural distance score of the event based on the first weight value, the second weight value, and context data of the event; and causing, by the hardware processor, display of the cultural distance score of the media asset on a user interface of a client device.
 22. The method of claim 21, comprising: determining the context data of the event based on the content data of the media asset.
 23. The method of claim 21, wherein the machine learning algorithm comprises at least one of a decision tree algorithm, a random forest algorithm, a graph neural network (GNN) algorithm, or a matrix factorization algorithm, and wherein the machine learning algorithm corresponds to a representation learning framework.
 24. The method of claim 21, comprising: after the cultural distance score is displayed on the user interface on the client device, receiving a feedback score from the client device, determining a difference between the feedback score and the cultural distance score, and adjusting the machine learning algorithm based on the difference.
 25. The method of claim 21, comprising: determining a level of predicted appeal of the event by the audience of the culture of destination based on a value of the cultural distance score.
 26. The method of claim 21, comprising: determining an element of the media asset based on the content data; generating a second cultural distance score by applying a machine learning algorithm to the element based on the first weight value and the second weight value, the cultural distance score being a first cultural distance score; and adjusting the first cultural distance score of the media asset based on the second cultural distance score.
 27. The method of claim 26, wherein the element is a theme or a genre of the media asset.
 28. The method of claim 21, wherein the media asset is a video content item or an audio content item.
 29. The method of claim 21, comprising: accessing metadata associated with the media asset, the metadata including a time-based element of the media asset, and adjusting the cultural distance score based on the time-based element.
 30. The method of claim 29, wherein the time-based element comprises at least one of a production year, an original release year, a consumption date, or a time period of content of the media asset.
 31. The method of claim 21, comprising: accessing metadata associated with the media asset, the metadata including a geographical knowledge-based element of the second geographical region; and adjusting the cultural distance score based on the geographical knowledge-based element.
 32. The method of claim 31, wherein the geographical knowledge-based element corresponds to at least one of a current policy, a law, or a news event associated with the second geographical region.
 33. The method of claim 32, comprising: detecting a change to the geographical knowledge-based element; and updating the second weight value of the cultural attribute category associated with the second geographical region based on the change to the geographical knowledge-based element.
 34. A system comprising. a memory storing instructions; and one or more hardware processors communicatively coupled to the memory and configured by the instructions to perform operations comprising: accessing content data for a media asset, the media asset, determining an event at a timestamp within the content data of the media asset, the event being relevant to a cultural attribute category, identifying a first geographical region corresponding to a culture of origin of the media asset and a second geographical region corresponding to a culture of destination of the media asset, the first geographical region being where content of the media asset was created, the second geographical region being where the media asset is targeted for release to an audience, accessing a first weight value of the cultural attribute category associated with the first geographical region; accessing a second weight value of the cultural attribute category associated with the second geographical region; using machine learning algorithm to generate a cultural distance score of the event based on the first weight value, the second weight value, and context data of the event; and causing display of the cultural distance score of the media asset on a user interface of a client device.
 35. The system of claim 34, wherein the operations comprise: after the cultural distance score is displayed on the user interface on the client device, receiving a feedback score from the client device, determining a difference between the feedback score and the cultural distance score, and adjusting the machine learning algorithm based on the difference.
 36. The system of claim 34, wherein the operations comprise: determining a level of predicted appeal of the event by the audience of the culture of destination based on a value of the cultural distance score.
 37. The system of claim 34, wherein the operations comprise. determining an element of the media asset based on the content data; generating a second cultural distance score by applying a machine learning algorithm to the element based on the first weight value and the second weight value, the cultural distance score being a first cultural distance score; and adjusting the first cultural distance score of the media asset based on the second cultural distance score.
 38. The system of claim 37, wherein the element is a theme or a genre of the media asset.
 39. The system of claim 34, wherein the operations comprise: accessing metadata associated with the media asset, the metadata including a time-based element of the media asset, and adjusting the cultural distance score based on the time-based element.
 40. A non-transitory computer-readable medium comprising instructions that, when executed by a hardware processor of a device, cause the device to perform operations comprising: accessing content data for a media asset; determining an event at a timestamp within the content data of the media asset, the event being relevant to a cultural attribute category, identifying a first geographical region corresponding to a culture of origin of the media asset and a second geographical region corresponding to a culture of destination of the media asset, the first geographical region being where content of the media asset was created, the second geographical region being where the media asset is targeted for release to an audience, accessing a first weight value of the cultural attribute category associated with the first geographical region; accessing a second weight value of the cultural attribute category associated with the second geographical region; using machine learning algorithm to generate a cultural distance score of the event based on the first weight value, the second weight value, and context data of the event; and causing display of the cultural distance score of the media asset on a user interface of a client device. 